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Digital Performance Management - 4 Key Metrics to Watch

Klaus Enzenhofer

Today's websites are not just marketing channels, they are critical production factors. If a website doesn't deliver a satisfactory customer experience the entire value delivery chain breaks down, and a company will not generate revenue regardless of product quality or value proposition.

Mastering digital performance is one of the leading challenges of the web economy, and requires a joint effort between IT and the lines of business. It means measuring and managing the end-to-end transaction delivery and translating it into actionable information. This will allow you to deliver an engaging digital experience, thus maximizing revenue and improving brand loyalty.

This gets a lot easier if you simply monitor a handful of key application performance metrics. This blog describes four good ones to get started with:

1. Make sure that your online business is actually generating revenue

Cyber Monday 2014 was Walmart's biggest ever online shopping event, with mobile driving 70% of total traffic. Application performance was a major factor impacting the business results; a recent study indicates the company experienced a 2% conversion increase for every one-second improvement in response time.

It's the responsibility of both the business and engineering teams to define and achieve conversion and revenue goals, and keeping an eye on these two metrics in real time is essential.

The first set of metrics to add to your dashboard are:

■ Revenue targets

■ Conversion Rate

■ A number, or count, of money-making actions

2. Make sure that your infrastructure is available to generate revenue

There is nothing worse than your system being unavailable. This frustrates customers and often drives them to a competitor's website! Kia and Soda Stream USA struggled with this issue during Super Bowl XLVIII. To address this risk, set up an availability check for your IT systems. This is inexpensive, easily implemented and does not require much in the way of significant IT changes.

The metric to add to your dashboard is:

■ Availability from my top locations

3. Be certain that every revenue-generating customer is a happy one

You can track and understand the user's journey based on their actions. This allows you to determine what the user did with your application, how long they worked with it, which features they used and how the overall experience with your company was delivered.

The metric to add to your dashboard is:

■ User Experience Index

4. Are your business critical actions successful, erroneous or slow?

The user experience index is a great metric to provide a general overview, but there are some other revenue-generating transactions like "search", "add to cart", "check out" and "pay" that you should also be plugged into. For financial services companies, key transactions like "log-in" and "transfer funds" can be added.

The metrics to add to your dashboard are:

■ Number of executions of the critical action

■ Failure rate per critical action

■ Response time per critical action

Conclusion

It's the responsibility of both the business and engineering teams to not only define conversion and revenue goals, but also make sure they are reached. In IT you can't impact the product portfolio or how it's marketed, but you can certainly make sure application performance doesn't become a roadblock. You want to eliminate all revenue barriers, and a focus on digital performance can insure that the road to conversion is quick and easy.

Klaus Enzenhofer is a Senior Technology Strategist in the Center of Excellence at Dynatrace.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Digital Performance Management - 4 Key Metrics to Watch

Klaus Enzenhofer

Today's websites are not just marketing channels, they are critical production factors. If a website doesn't deliver a satisfactory customer experience the entire value delivery chain breaks down, and a company will not generate revenue regardless of product quality or value proposition.

Mastering digital performance is one of the leading challenges of the web economy, and requires a joint effort between IT and the lines of business. It means measuring and managing the end-to-end transaction delivery and translating it into actionable information. This will allow you to deliver an engaging digital experience, thus maximizing revenue and improving brand loyalty.

This gets a lot easier if you simply monitor a handful of key application performance metrics. This blog describes four good ones to get started with:

1. Make sure that your online business is actually generating revenue

Cyber Monday 2014 was Walmart's biggest ever online shopping event, with mobile driving 70% of total traffic. Application performance was a major factor impacting the business results; a recent study indicates the company experienced a 2% conversion increase for every one-second improvement in response time.

It's the responsibility of both the business and engineering teams to define and achieve conversion and revenue goals, and keeping an eye on these two metrics in real time is essential.

The first set of metrics to add to your dashboard are:

■ Revenue targets

■ Conversion Rate

■ A number, or count, of money-making actions

2. Make sure that your infrastructure is available to generate revenue

There is nothing worse than your system being unavailable. This frustrates customers and often drives them to a competitor's website! Kia and Soda Stream USA struggled with this issue during Super Bowl XLVIII. To address this risk, set up an availability check for your IT systems. This is inexpensive, easily implemented and does not require much in the way of significant IT changes.

The metric to add to your dashboard is:

■ Availability from my top locations

3. Be certain that every revenue-generating customer is a happy one

You can track and understand the user's journey based on their actions. This allows you to determine what the user did with your application, how long they worked with it, which features they used and how the overall experience with your company was delivered.

The metric to add to your dashboard is:

■ User Experience Index

4. Are your business critical actions successful, erroneous or slow?

The user experience index is a great metric to provide a general overview, but there are some other revenue-generating transactions like "search", "add to cart", "check out" and "pay" that you should also be plugged into. For financial services companies, key transactions like "log-in" and "transfer funds" can be added.

The metrics to add to your dashboard are:

■ Number of executions of the critical action

■ Failure rate per critical action

■ Response time per critical action

Conclusion

It's the responsibility of both the business and engineering teams to not only define conversion and revenue goals, but also make sure they are reached. In IT you can't impact the product portfolio or how it's marketed, but you can certainly make sure application performance doesn't become a roadblock. You want to eliminate all revenue barriers, and a focus on digital performance can insure that the road to conversion is quick and easy.

Klaus Enzenhofer is a Senior Technology Strategist in the Center of Excellence at Dynatrace.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...